Multidetector-row CT of lung cancer screening

The Value of a Seven-Autoantibody Panel Combined with the Mayo Model in the Differential Diagnosis of Pulmonary Nodules

Background

Identifying malignant pulmonary nodules and detecting early-stage lung cancer (LC) could reduce mortality. This study investigated the clinical value of a seven-autoantibody (7-AAB) panel in combination with the Mayo model for the early detection of LC and distinguishing benign from malignant pulmonary nodules (MPNs).

Methods

The concentrations of the elements of a 7-AAB panel were quantitated by enzyme-linked immunosorbent assay (ELISA) in 806 participants. The probability of MPNs was calculated using the Mayo predictive model. The performances of the 7-AAB panel and the Mayo model were analyzed by receiver operating characteristic (ROC) analyses, and the difference between groups was evaluated by chi-square tests (χ²).

Results

The combined area under the ROC curve (AUC) for all 7 AABs was higher than that of a single one. The sensitivities of the 7-AAB panel were 67.5% in the stage I-II LC patients and 60.3% in the stage III-IV patients, with a specificity of 89.6% for the healthy controls and 83.1% for benign lung disease patients. The detection rate of the 7-AAB panel in the early-stage LC patients was higher than that of traditional tumor markers. The AUC of the 7-AAB panel in combination with the Mayo model was higher than that of the 7-AAB panel alone or the Mayo model alone in distinguishing MPN from benign nodules. For early-stage MPN, the sensitivity and specificity of the combination were 93.5% and 58.0%, respectively. For advanced-stage MPN, the sensitivity and specificity of the combination were 91.4% and 72.8%, respectively. The combination of the 7-AAB panel with the Mayo model significantly improved the detection rate of MPN, but the positive predictive value (PPV) and the specificity were not improved when compared with either the 7-AAB panel alone or the Mayo model alone.

Conclusion

Our study confirmed the clinical value of the 7-AAB panel for the early detection of lung cancer and in combination with the Mayo model could be used to distinguish benign from malignant pulmonary nodules.

Comparison of four models predicting the malignancy of pulmonary nodules: A single-center study of Korean adults

Objective

Four commonly used clinical models for predicting the probability of malignancy in pulmonary nodules were compared. While three of the models (Mayo Clinic, Veterans Association [VA], and Brock University) are based on clinical and computed tomography (CT) characteristics, one model (Herder) additionally includes the ¹⁸F-fluorodeoxyglucose (FDG) uptake value among the positron emission tomography (PET) characteristics. This study aimed to compare the predictive power of these four models in the context of a population drawn from a single center in an endemic area for tuberculosis in Korea.

Methods

A retrospective analysis of 242 pathologically confirmed nodules (4–30 mm in diameter) in 242 patients from January 2015 to December 2015 was performed. The area under the receiver operating characteristic curve (AUC) was used to assess the predictive performance with respect to malignancy.

Results

Of 242 nodules, 187 (77.2%) were malignant and 55 (22.8%) were benign, with tuberculosis granuloma being the most common type of benign nodule (23/55). PET was performed for 227 nodules (93.8%). The Mayo, VA, and Brock models showed similar predictive performance for malignant nodules (AUC: 0.6145, 0.6042 and 0.6820, respectively). The performance of the Herder model (AUC: 0.5567) was not significantly different from that of the Mayo (vs. Herder, p = 0.576) or VA models (vs. Herder, p = 0.999), and there were no differences among the three models in determining the probability of malignancy of pulmonary nodules. However, compared with the Brock model, the Herder model showed a significantly lower ability to predict malignancy (adjusted p = 0.0132).

Conclusions

In our study, the Herder model including the ¹⁸FDG uptake value did not perform better than the other models in predicting malignant nodules, suggesting the limited utility of adding PET/CT data to models predicting malignancy in populations within endemic areas for benign inflammatory nodules, such as tuberculosis.

Breast CT

Breast cancer is a serious disease that accounts for approximately 40,000 deaths per year in the United States. Unfortunately, there is no known cause of breast cancer, and therefore the best way to prevent mortality is early detection. In the past 15 years, breast cancer mortality has been reduced significantly, which is in part due to screening with film-screen mammography. Nonetheless, conventional mammography lacks sensitivity, especially for certain subgroups of women such as those with dense breast tissue, those under 50 years old, and pre- or perimenopausal women. In addition, mammography has a very poor positive predictive value for biopsy, with 70%-90% of biopsies performed turning out negative. By improving visualization of breast tissue, X-ray computerized tomography (CT) of the breast can potentially provide improvements in diagnostic accuracy over conventional mammography. Owing to recent technological developments in digital detector technology, flat-panel CT imagers dedicated to imaging of the breast are now feasible. A number of academic groups are currently researching dedicated breast CT and prototype systems are currently being evaluated in the clinical setting.

Low-dose multidetector dynamic CT in the breast

This study investigated the feasibility of using low-dose multidetector dynamic computed tomography (CT) scan for imaging breast. We measured the radiation dose using a phantom at low- and standard-dose CT. To compare the image quality at low- and standard-dose CT, we evaluated normal breasts in 57 cases. In 44 cases with breast cancer, we assessed the staging and time-enhancement curves of breast cancer. In conclusion, the low-dose multidetector dynamic CT scan is feasible for the evaluation of the breast, with reduced radiation dose and with similar image quality when compared with standard-dose CT scan. In breast cancers, low-dose dynamic CT could be used for the staging of breast cancer before surgery.